프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

Trustrails MCP

제작자: james-webdev버전 1.0.28i★ –i내 컴퓨터에서 실행
레지스트리 이름app.trustrails/server

설치

개요

제작자가 쓴 설명(영어 원문)

Search UK electronics products across retailers: prices, stock and specs, refreshed twice a day.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 검색 및 AI 기억
  • 입력해야 할 값: TRUSTRAILS_API_KEY

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 직접 입력할 값: TRUSTRAILS_API_KEY

비밀로 표시된 값은 내 계정의 열쇠예요. 절대 다른 사람과 공유하거나 채팅에 붙여 넣지 마세요.

필요한 것

  • npmNode.js 설치(npx 명령어용)

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "mcpServers": {
    "server": {
      "command": "npx",
      "args": [
        "-y",
        "@trustrails/mcp-server"
      ],
      "env": {
        "TRUSTRAILS_API_KEY": "your-value"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add server --env TRUSTRAILS_API_KEY=your-value -- npx -y @trustrails/mcp-server

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "server": {
      "command": "npx",
      "args": [
        "-y",
        "@trustrails/mcp-server"
      ],
      "env": {
        "TRUSTRAILS_API_KEY": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "server": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@trustrails/mcp-server"
      ],
      "env": {
        "TRUSTRAILS_API_KEY": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

직접 입력할 값

  • TRUSTRAILS_API_KEY 비밀 env
    Optional TrustRails API key. If unset, the server uses the shared public key, which is rate-limited.

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 도메인 trustrails.app 기준으로 확인되었어요(레지스트리가 도메인 소유를 확인해요)

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 7231개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 7231개 중 57%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 7231개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

Trustrails MCP, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

입력해야 할 값: TRUSTRAILS_API_KEY

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(1.0.28)은 27일 전 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT3일 전 업데이트
설치

Codebase Memory

제작자: DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

내 컴퓨터에서 실행★ 48,148MIT20일 전 업데이트
설치

Exa

제작자: exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

클라우드에서 실행★ 5,213MIT44일 전 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

TrustRails MCP Server

Search UK electronics products - compare prices, find deals, and discover products across multiple retailers.

Built for the Model Context Protocol (MCP) - works with Claude Desktop, Claude Code, and other MCP-compatible AI assistants.


Quick Start

No installation needed — just add TrustRails to your Claude config.

Configuration

For Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{

"mcpServers": { "trustrails": { "command": "npx", "args": ["-y", "@trustrails/mcp-server"], "env": { "TRUSTRAILS_API_KEY": "mcp-public-2026" } } } }

For Claude Code (~/.config/claude/config.json):

{

"mcpServers": { "trustrails": { "command": "npx", "args": ["-y", "@trustrails/mcp-server"], "env": { "TRUSTRAILS_API_KEY": "mcp-public-2026" } } } }

That's it! Restart Claude and start searching.


What You Can Do

Natural Language Product Search

Just ask Claude naturally — it will decompose your request into the right query and filters:

"Find me a gaming laptop under £1000"

"I need Sony noise cancelling headphones" "What HP laptops are available between £500-£700?" "Show me Anker chargers"

Claude will search across multiple UK retailers and show you:

  • Real-time prices & availability
  • Purchase links (trustrails.app/go/ redirects to the retailer, through an affiliate link)
  • Every known structured spec (attributes), so products can be compared from the search alone
  • Then get_product for your final 1-3 picks, only when you need what search lacks: the retailer's description, or every retailer's offer and buy link (search carries only the best offer's purchase_url, cheapest in stock)

Available Tools

search_products

Search 26,000+ UK electronics products across 7 retailers with price comparison. Prices are in GBP. The tool text tells the assistant to split the request into filters first: brand, category and price go in their own arguments, RAM, storage, screen size, resolution, refresh rate, wattage and Wi-Fi generation go in constraints, and only what is left over goes in query.

Parameters:

  • query (string, optional) - Refinement terms ONLY: model lines, series, variants, model numbers (e.g. 'neo', 'ultra', 'oled', 'WH-1000XM5', 's25 ultra'). Never a category name (BAD query='tablet', query='smartwatch', query='laptop': set the category filter instead), never a brand name (BAD query='Sony': set the brand filter instead), never a price. Omit entirely when browsing a category or brand: 'show me tablets' = category='Tablets', no query. Query words must appear in the title, except words naming the category ('router' in Networking) and bare numbers or specs ('4070', '16GB'), which are ignored when finding products and only rank them. Put a model number with its prefix ('RTX 4070', not '4070') and check each title for it. Leave out use-case words like gaming, cheap or best.
  • min_price (number, optional) - Minimum price in GBP.
  • max_price (number, optional) - Maximum price in GBP.
  • brand (string, optional) - Filter by brand name (exact match, case-insensitive). Examples: Apple, Samsung, Sony, HP, Dell, Lenovo, Anker, Bose, LG
  • category (string, optional) - Filter by product category. Use ONLY these exact values: Laptops, Desktops, Tablets, Phones, TVs, Monitors, Headphones, Speakers, Cameras, Keyboards, Mice, Printers, Networking, Storage, Gaming, Wearables, Drones, Audio, Cables & Chargers. 'Smartphones' is not valid (use 'Phones'), nor is 'Televisions' (use 'TVs'). Gaming headsets are category='Headphones', query='gaming headset': the Gaming category is consoles, controllers and accessories only.
  • constraints (object, optional) - Hard spec requirements, checked per product against its attributes. Shape {name: {op: number}} with op eq, gte or lte; a range is {gte, lte}. On every operator, storage matches within 3% and screen size within 0.5 inch; a whole-number screen size N also covers up to N+1 on eq and lte (gte is unchanged); other specs exactly: gte 1024 accepts a 1TB drive, eq 22 a 21.5" screen, eq 13 a 13.6" one, lte 15 a 15.6" one. Example: {"memory_gb": {"gte": 24}, "storage_gb": {"gte": 1000}, "screen_in": {"eq": 15}}. Names and units: memory_gb (RAM, GB), storage_gb (GB, 1TB = 1000), screen_in (inches), resolution_p (pixels high: 4K = 2160, QHD = 1440, Full HD = 1080), refresh_hz (Hz), power_w (W), wifi_gen (Wi-Fi generation: 6, 6E = 6.5, 7). A spec written in query counts only when it says what it is ('24GB RAM', '1TB', '144Hz', '55"') and means at least, except screen size (that size); a bare '24GB' stays a search word. Explicit constraints win over it. A plain 16GB or 65W is gte; use eq only when the user says exactly, and for screen size. Always set category (and brand if known) with constraints: a search of only specs is rejected, as is a bad request (unknown name or operator, negative value, a combination nothing can meet such as gte above lte); the error lists the valid ones. See Structured specs.
  • lite (boolean, optional) - Return trimmed product objects with only essential fields (id, title, brand, price, currency, availability, image_url, purchase_url, offer_count, attributes (every known spec as {status, value}, without sources) and, with constraints, constraint_status). Always set to true; it still carries attributes. Use false only for ean, category, provenance or all sources at once.
  • limit (number, optional) - Maximum number of products to return (default 50, max 100)
  • sort (string, optional) - Sort order: 'relevance' (default), 'price_asc' (in stock first, then cheapest), 'price_desc' (in stock first, then most expensive). With constraints, matched products still come first.

Returns: products and a total. Each product carries every known attributes spec (a product with no attributes key has none of these seven specs known: say so only if the user asked about one): compare specs from these. State the value of a confirmed or inferred spec; only conflicting (retailers disagree) or a missing name (unknown) needs a caveat. With constraints, each product has constraint_status per name (matched: a retailer's title states a value that meets it; unverified: not known to meet it, so never treat it as a match), total counts the products that match every constraint, unverified_total the unverified ones that passed the other filters, and excluded_by_constraints the products whose stated value fails. candidates_truncated: true means the first 2,000 candidates in the chosen sort order were checked and more exist: add a brand or category, or a narrower query, and search again. availability is in_stock, low_stock, out_of_stock or unknown (the retailer gave no stock signal), and with purchase_url and price it comes from the product's best offer: in stock first, then cheapest. If offer_count is above 1, call get_product for your final 1-3 picks to compare retailers. Before claiming a saving, read offers[].title (from get_product): they must name the same model; if they differ, don't claim one. Before recommending any pick, check its title is the product type asked for (not a cable, accessory or adapter).

get_product

Get full details for a single product by ID. Returns structured attributes (see Structured specs), specs.description (the retailer's own prose, for processor, GPU, ports, weight, battery and features that attributes does not cover), pricing, availability, delivery time, and all retailer offers with per-retailer pricing (offers[].stock is a unit count only when the retailer supplies one, otherwise null). Accepts both canonical product IDs and original retailer offer IDs. Use after search_products only for your final 1-3 picks, and only when you need what search lacks: details only the description has (if it doesn't give a detail, tell the user it isn't listed), or every retailer's offer and buy link (search carries only the best offer's purchase_url, cheapest in stock). Search results already carry attributes to compare specs. A pick with offer_count 1 needs no call unless the user asked for a description-only detail.

Parameters:

  • product_id (string) - The unique product ID from search results

Returns: Complete product with structured attributes, specs (including the retailer's specs.description), offers sorted in stock first then cheapest, and provenance information

Structured specs

Search results and get_product return attributes: structured specs (RAM, storage, screen size, resolution, refresh rate, power, Wi-Fi generation) read from retailer titles when the catalogue is imported. Each has a status, and a spec nobody states is absent (unknown); a product with no attributes key has none of these seven specs known (say so only if the user asked about one). What each call returns:

CallReturns
search_products with lite=true (what agents should always set)id, title, brand, price, currency, availability, image_url, purchase_url, offer_count, every known attribute as {status, value} (or {status, values} when conflicting) without sources, and constraint_status when constrained
search_products without litethe lite fields plus ean, category, product_type, provenance and every attribute with its sources
get_producteverything, including specs.description and dimensions, offers and delivery_time

Names and units are as in constraints. State the value for confirmed and inferred; only conflicting or a missing name needs a caveat. The statuses:

  • confirmed: two or more retailers state the same value.
  • inferred: one retailer's title states it.
  • conflicting: retailers state different values. None is picked; the values are listed with their retailers, and a retailer can appear under two values (check offers[].title).

specs.description is the retailer's prose. It can describe another configuration or a maximum ("up to 32GB"), so it never overrides or fills in an attribute: a spec missing from attributes is unknown.

For one Galaxy S26 Ultra, where only JoyBuy states the RAM (12GB) and two retailers disagree about the storage:

{

"memory_gb": { "status": "inferred", "value": 12, "sources": [{ "retailer": "JoyBuy", "field": "title" }] }, "storage_gb": { "status": "conflicting", "values": [ { "value": 256, "sources": [{ "retailer": "Laptops Direct", "field": "title" }] }, { "value": 512, "sources": [{ "retailer": "JoyBuy", "field": "title" }] } ] } }

Searches with constraints return the constraints applied, excluded_by_constraints, unverified_total, and a constraint_status on each product: matched (a retailer's title states a value that meets the constraint) or unverified (not known to meet it: never treat as a match, tell the user it is unconfirmed; check attributes[name], where conflicting means retailers disagree and missing means unknown). Products whose stated value fails a constraint are left out. A lite result carries the status and value of every known spec, without sources, so a 64GB laptop is told apart from a 16GB one and disagreeing from unknown.

  • Tolerance: storage within 3% (1TB = 1000–1024GB) and screen size within 0.5 inch, on every operator. Retailers round sizes down, so a whole-number screen size N also covers up to N+1 on eq and lte (eq 13 ac

앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요

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